The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models
This article proposed a novel classification framework that can classify the samples of multiple domains based on the outputs of multiple models. Different from the existing methods that train single model on all domains, our framework trains multiple models on each domain. On a testing sample, the...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2022/5578043 |
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| _version_ | 1832562048863567872 |
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| author | Qingzeng Song Junting Xu Lei Ma Ping Yang Guanghao Jin |
| author_facet | Qingzeng Song Junting Xu Lei Ma Ping Yang Guanghao Jin |
| author_sort | Qingzeng Song |
| collection | DOAJ |
| description | This article proposed a novel classification framework that can classify the samples of multiple domains based on the outputs of multiple models. Different from the existing methods that train single model on all domains, our framework trains multiple models on each domain. On a testing sample, the outputs of all trained models are used to predict the domain of this sample. Then, this sample is classified by the output of models that belong to the predicted domain. Experiments show that our framework achieved higher accuracy than the existing methods. Furthermore, our framework achieves good scalability on multiple domains. |
| format | Article |
| id | doaj-art-611b40d9edbe4e0d8980662e94d6b644 |
| institution | Kabale University |
| issn | 1099-0526 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-611b40d9edbe4e0d8980662e94d6b6442025-02-03T01:23:34ZengWileyComplexity1099-05262022-01-01202210.1155/2022/5578043The Classification of Multi-Domain Samples Based on the Cooperation of Multiple ModelsQingzeng Song0Junting Xu1Lei Ma2Ping Yang3Guanghao Jin4School of Computer Science and TechnologySchool of Computer Science and TechnologySchool of Telecommunication EngineeringSchool of Computer Science and TechnologySchool of Telecommunication EngineeringThis article proposed a novel classification framework that can classify the samples of multiple domains based on the outputs of multiple models. Different from the existing methods that train single model on all domains, our framework trains multiple models on each domain. On a testing sample, the outputs of all trained models are used to predict the domain of this sample. Then, this sample is classified by the output of models that belong to the predicted domain. Experiments show that our framework achieved higher accuracy than the existing methods. Furthermore, our framework achieves good scalability on multiple domains.http://dx.doi.org/10.1155/2022/5578043 |
| spellingShingle | Qingzeng Song Junting Xu Lei Ma Ping Yang Guanghao Jin The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models Complexity |
| title | The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models |
| title_full | The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models |
| title_fullStr | The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models |
| title_full_unstemmed | The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models |
| title_short | The Classification of Multi-Domain Samples Based on the Cooperation of Multiple Models |
| title_sort | classification of multi domain samples based on the cooperation of multiple models |
| url | http://dx.doi.org/10.1155/2022/5578043 |
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